As manufacturing systems become increasingly automated, the control systems required to ensure the proper operation of such systems become correspondingly complex. In general, most control systems comprise a variety of sensors that provide measurements of a desired quantity to a computer processor. The processor in turn calculates the settings for a variety of actuators that change the operation of the system so that the sensors produce a desired reading.
As the number of sensors and actuators used in a manufacturing system increase, the numerical calculations that must be performed by the computer processor to calculate the proper actuator settings become increasingly complex. Therefore, a great deal of research and development has been directed to methods of simplifying the calculations that are required to compute the setting for the actuators. In the past, these simplified calculations used a fixed set of assumptions regarding the type of disturbances that may affect the operation of the manufacturing system. While this method works well for systems whose disturbances are well known, there are many situations where the type of disturbances cannot be predicted. Therefore the reduced model of the underlying system may be inaccurate.
Given the shortcomings in the prior art, there is a need for a control system that can perform control calculations for a large number of sensors and actuators in real time while simultaneously optimizing a reduced order model for the disturbances that actually occur.